With modern requirements, there is an increasing tendency of considering multiple
objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a …

In contrast to the traditional single-tasking evolutionary algorithms, evolutionary multitasking
(EMT) travels in the search space of multiple optimization tasks simultaneously. Through …

Multitask optimization aims to solve two or more optimization tasks simultaneously by
leveraging intertask knowledge transfer. However, as the number of tasks increases to the …

Multitasking optimization can achieve better performance than traditional single-tasking
optimization by leveraging knowledge transfer between tasks. However, the current …

Replication studies increase our confidence in previous results when the findings are similar
each time, and help mature our knowledge by addressing both internal and external validity …

Many recent studies have shown that various multi-objective evolutionary algorithms have
been widely applied in the field of search-based software engineering (SBSE) for optimal …

There is evidence that many real-world applications can be characterized as sparse
multiobjective problems (SMOPs), where most variables of their Pareto optimal solutions are …

In this paper we provide empirical evidence that the rating that an app attracts can be
accurately predicted from the features it offers. Our results, based on an analysis of 11,537 …

Software effort estimation studies still suffer from discordant empirical results (ie, conclusion
instability) mainly due to the lack of rigorous benchmarking methods. So far only one …

One main challenge in multi-objective Bayesian optimization of expensive problems is that
only a very limited number of fitness evaluations can be afforded. To address the above …